Senior Cloud Data Engineer at Hollard Insurance

Required Knowledge and Experience    

Your duties & responsibilities

• Reporting to the Data Engineer Practice Manger your key focus areas will be:
• You will be required to design, build, and support the delivery of modern data engineering solutions in the Azure data platform, collaborating closely with cross-functional teams within Hollard, including the Infrastructure DevOps team, solution architects, subject matter experts, data modelers, etc. This collaboration aims to ensure that Hollard’s data platform ecosystem is optimal in supporting business needs. Additionally, you will work collaboratively with vendors to ensure that data engineering delivery and practices meet Hollard’s standards.

Your duties and responsibilities include:

• Maintain, support, and monitor existing production Azure Synapse data engineering pipelines, PySpark notebooks, data platform lake house architecture and Azure SQL ODS storage and Stored Procedures, CICD pipelines to make sure all data loads on data platform meet data quality standards and business SLA requirements.
• Participate and contribute to Azure data platform architecture design, data modelling, gathering and analysis of data requirements; understand document, communicate and, build appropriate solutions.
• Participate, design, build, deliver, and document data platform related projects with latest Azure data analytics technologies.
• Promote data engineering best practices with CICD YAML pipelines and automation.
• Coach and mentor junior data engineers on Azure technologies.
• Knowledge acquired awareness, skills and/or expertise
• 10+ year demonstrable experience in design, build and support of data engineering pipelines in the data warehousing, BI analytics, and/or ML space, including 5+ year work experience using Azure data analytics tool such as Data Factory, Synapse, Azure SQL, data lake gen2, key vault.
• Strong knowledge and extensive experience in implementing modern lake house architecture on Azure Data Lake Gen2 and Apache Spark using PySpark.
• Strong knowledge and experience in metadata driven data engineering pipeline design and development towards reusable patterns and frameworks.
• Strong data modelling knowledge and physical implementation experiences with Dimensional and/or Data Vaults methodologies.
• Strong knowledge and extensive experience in working in an Agile framework with CI/CD using modern DevOps / Data Ops integrated processes with YAML pipelines
• Knowledge of machine learning, MLOps, AI is desirable
• Knowledge of IaC such as BICEP and/or Terraform is desirable
• Experience of manipulating semi-structured data (XML, JSON)
• Behavioural Competencies personal qualities
• Be available and engaged, flexible and resourceful with a can-do attitude.
• Passion for continuous learning with curiosity for technology and analytics
• Excellent communicator, collaborator and “people person” with an ability to build rapport quickly.
• Ability to effectively manage challenging situations without loss of focus when under pressure.
• Be comfortable with ambiguity and willing to get outside of comfort zone while delivering tangible results.    

Educationa Requirements

• Bachelor’s or Master’s degree in Computer/Data Science Technical or related field is a must. Post-graduate is highly regarded.
• Microsoft Azure Data Engineering Associate (DP-203) certification.